A guide for data-scientists to build data-products that can scale. I'll outline best-practices for working with data of all shapes and sizes using tools that are (mostly) free-for-use.
Broadly, we'll talk about
- Setting up infrastructure on Cloud Providers like AWS, Google, DigitalOcean ...
- Using Docker for maintaining reproducible compute environments
- Git and collaboration patterns in data-science teams
- Coding Standards to ensure code written by one is readable by all
- Testing and Packaging Code for automated deployment
- Design language for creating visualizations
- more ...